What Are Python Libraries for Data Science?

Written by Coursera Staff • Updated on

Learn about what Python libraries for data science are, which are popular, their uses, what types of professionals leverage them, their pros and cons, and how you can begin using them.

[Featured Image] A data science employee sits at a laptop at a table and explores the various Python libraries that she can use for her job.

Python was first released in 1991, and it has become a go-to language among programmers and professionals in varying industries for data science purposes. Python’s popularity comes from its ease of use, portability, robust community, flexibility, and available libraries capable of handling complex tasks related to data science.

Python libraries allow you to accomplish tasks and run data analysis more efficiently by providing portions of crucial code already built for you. Libraries capable of tackling data analysis tasks, such as data cleansing, manipulation, and creating visualizations, all exist for you to leverage. In fact, professionals know Python for its impressive amount of data science libraries for users, with the total being above 137,000 [1].

Due to the large number of libraries available for data science purposes, you may need help knowing which ones to choose. As a first step in your professional journey using Python libraries for data science, it may help you learn about a few of the most popular options and their various uses. 

Types of Python libraries for data science

Python has many libraries available to aid your programming and help you accomplish tasks more efficiently. These libraries come with a portion of code already written in the form of modules. Below are six popular Python libraries for data science, with a description of each to describe their uses and value.


The NumPy library focuses on mathematical capabilities and serves as the base for various other Python libraries for data science. NumPy is a popular library that grants you impressive computation abilities, the freedom to run analysis on data with multiple dimensions, and tools necessary for linear algebra analysis. The code contained in the package is from C, not Python, contributing to NumPy’s impressive speed.

C is a widely used, general-purpose program language applicable to many disciplines in computing. C is a compiled programming language, allowing it to have more speed and efficiency in executing code. Contrarily, Python is an interpreted programming language. Interpreted languages run code one line at a time and have less speed than compiled languages. In the case of NumPy, the code to build the library is in C. So, when you use NumPy in Python, you benefit from faster code while having simple Python syntax.


As a data scientist, you frequently create visualizations to showcase important data to key stakeholders and contribute to decision-making. Tailored for creating data visualizations, Matplotlib offers you many options for what graphs you can generate and how you can customize them. This library is free to use, open-source, and commonly built on top of other libraries. Matplotlib supports animated and highly interactive visualizations, as well as standard visualizations, in the form of bar charts, pie charts, box plots, error charts, and more.


Without generating a large quantity of code yourself, Pandas allows you to conduct data analysis, manipulation, and cleansing on your data set. Similar to NumPy, the code in Pandas is also from C, allowing you to benefit from its speed and flexibility. A few of its main features include the ability to download and transform your data, write additional data, and perform analysis on it. Pandas' abilities apply to various industries and fields, showing its prominence in data manipulation.


The SciPy Python data science library excels in data optimization and integration. Tailored to handle complex mathematical concepts related to data science and scientific computing, such as differential equations, this library provides the tools to quickly determine a solution for all your complex problems. SciPy provides value with various other topics you may come across, like:

  • Interpolation

  • Algebraic equations

  • Eigenvalue problems

  • High-level data structures


PyTorch delves into the topics of machine learning and deep learning by providing a foundation to create advanced models in machine learning efficiently. It has the capability to guide you through the full process of producing prototypes and releasing your models in production. PyTorch additionally has distributed training, natural language processing features, a large community to leverage, and related tools, such as TorchScript and TorchServe, to aid your model development process. Some of the world's largest prominent universities and corporations use PyTorch as a framework. 


Alongside Matplotlib, Seaborn is another popular data visualization library for Python. Not only are they similar, but Seaborn was actually built using the fundamentals of Matplotlib to provide users with the ability to generate more advanced and interactive graphs and charts. Seaborn features a high-level interface and advanced algorithms to create stunning visualizations from your entire set of available data.

What are Python libraries for data science used for?

Data science-focused libraries in Python have many uses and applications for professionals today. Topics related to data science and machine learning, such as data manipulation, data visualization, and data analysis, are some popular ones related to these libraries. Below, you will find a brief description of how subtopics of data science leverage these Python libraries in the real world.

Machine learning

In general, machine learning is a type of artificial intelligence (AI) that uses advanced approaches through algorithms, data analysis, and statistical models to simulate the way humans think and retain information. The goal of machine learning is to train a model to make accurate predictions in various situations, which can then be used as a tool to aid decision-making.

Python and its various data science libraries provide a framework for building these machine learning models. Python's features allow for easy data validation, cleansing, processing, and analysis. Since Python libraries for data science come with important code already in place, you have to worry less about the technical aspects of coding, where costly errors may occur.

Automated machine learning (AutoML)

AutoML builds upon the ideas of traditional machine learning and aims to “automate” the repeated and lengthy steps involved with training and building a model. This enables you to create top-tier machine learning models at an efficient pace by using algorithms to handle the iterative parts of the building process.

Auto-PyTorch and Auto-Sklearn are two Python libraries for data science specifically geared towards facilitating AutoML. Auto-PyTorch caters to you by offering full automation in critical areas and the ability to work with in-depth neural networks. Auto-Sklearn leverages meta-learning and a few other techniques to pinpoint the exact algorithm you need to train your model based on the characteristics of your input data. 

Deep learning

As a subtopic of machine learning, deep learning involves replicating how humans think through simulations and deep neural networks. Deep learning aims to train models with mass quantities of data to optimize prediction-making capabilities.

Python libraries, such as TensorFlow and Keras, enable you to conduct deep learning. Keras, in particular, combines other popular Python libraries to create a user-friendly environment for handling neural networks. 

Natural language processing

Natural language processing aims to accurately decipher the human language through various algorithms and models. It does this by separating speech into smaller segments and exploring the connections and relationships involved with each part to try and discover the overall message. An important benefit of natural language processing is how it improves communication with computers.

Many Python libraries for data science exist to explore natural language processing, such as NLTK, TextBlob, and spaCy. These libraries allow you to create applications capable of classification, sentiment analysis, tokenization, and more fairly easily.

Who uses Python libraries for data science?

Due to the versatility and ease of use inherent in Python and the mass amount of data science libraries available, professionals in many disciplines and industries, such as statistics, mathematics, data science, and business, leverage these tools. Examples of relevant industries and fields, beyond those mentioned already, related to Python libraries for data science include:

  • Web development

  • Computer vision

  • Game development

  • Biology

  • Psychology

  • Medicine

  • Robotics

  • Autonomous vehicles

Python features a vast community of expert programmers, social scientists, data scientists, machine learning developers, and others who use Python libraries for data science and are interested in helping you solve problems. 

Pros and cons of using Python libraries for data science

Utilizing Python for data science comes with various pros and cons. Understanding the pros and cons of Python allows you to determine what cases it is best suited for and if it can help you achieve the tasks you are handling. A few of the pros and cons reference the R programming language. R is a popular language utilized for statistical analysis and data science, similar to Python. R specializes in statistical models, analysis, and building graphs and other visualizations.


The pros of using Python libraries for data science include:

  • Popularity and versatility as a universal coding language

  • Ease of use

  • Not a steep learning curve

  • Open source

  • Enables quick development

  • Relevant for a wide range of jobs

  • Large community of users

  • Robust standard libraries

  • Ease of reproducibility


The cons of using Python libraries for data science include:

  • Inability to efficiently handle large data sets

  • Slow computation

  • Runtime errors are common

  • Lacking memory efficiency

  • Harder to work with databases

  • Other programming languages, including R, have more data science libraries

  • Commonly overused or used in the wrong contexts or situations

  • Less informative visualizations, compared to R

How to get started using Python libraries for data science

You can utilize Python libraries for data science by ensuring you have the necessary skills for this discipline. A strong background in math or statistics can help you build your skills in data science. The next is to become familiar with coding in Python by becoming familiar with the basic syntax and available libraries.

This foundation gives you the necessary experience in Python and data science topics to utilize Python libraries for data science. Various educational options exist for you to pursue to begin learning Python for data science, including completing a bachelor’s or master’s degree in data science or attending a data science bootcamp. Many bootcamps tailored for data science are options for you to build your skills, and a few popular options include:

  • Bloom Institute of Technology (formerly known as Lambda School)

  • Thinkful

  • Byte Academy

  • Flatiron School

Getting started on Coursera

To learn more about Python libraries for data science or other Python topics in general, it can be helpful to complete a course or receive a relevant certificate. For example, Coursera offers Data Analysis with Python by IBM. This course allows you to gain experience cleaning and preparing data, executing exploratory data analysis, building data pipelines, and handling data frames. It also features Python data science libraries, such as Pandas, Numpy, and Scipy, for you to conduct analysis with. 

Another course to consider is the Applied Data Science with Python Specialization from the University of Michigan. This specialization features five unique courses, exposing you to inferential statistical analysis, applied machine learning, network connectivity, and the pros and cons of data visualizations.

Article sources

  1. University of Michigan. “Installing Libraries and Packages, https://docs.support.arc.umich.edu/python/pkgs_envs/.” Accessed December 8, 2023. 

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